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시험패스가능한MLS-C01높은통과율시험대비덤프공부뎜프데모

2025 Fast2test 최신 MLS-C01 PDF 버전 시험 문제집과 MLS-C01 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1xiS5w3R54us6Wx4V7jCwmQC0RwI602KG

Fast2test의Amazon인증 MLS-C01시험덤프 공부가이드는 시장에서 가장 최신버전이자 최고의 품질을 지닌 시험공부자료입니다.IT업계에 종사중이라면 IT자격증취득을 승진이나 연봉협상의 수단으로 간주하고 자격증취득을 공을 들여야 합니다.회사다니면서 공부까지 하려면 몸이 힘들어 스트레스가 많이 쌓인다는것을 헤아려주는Fast2test가 IT인증자격증에 도전하는데 성공하도록Amazon인증 MLS-C01시험대비덤프를 제공해드립니다.

AWS 인증 기계 학습 - 전문가 시험은 준비와 공부를 필요로 하는 도전적이고 포괄적인 자격증 프로그램입니다. 이 시험은 65개의 객관식 및 다중응답 문제로 구성되어 있으며, 시험 시간은 180분입니다. 시험 비용은 300달러이며, 영어, 일본어, 한국어 및 중국어 간체 등 다양한 언어로 제공됩니다.

>> MLS-C01높은 통과율 시험대비 덤프공부 <<

시험패스에 유효한 MLS-C01높은 통과율 시험대비 덤프공부 최신버전 덤프

최근 Amazon인증 MLS-C01시험이 IT업계에서 제일 높은 인지도를 가지고 있습니다.바라만 보지 마시고Amazon인증 MLS-C01시험에 도전해보세요. Fast2test 의 Amazon인증 MLS-C01덤프로 시험준비공부를 하시면 한방에 시험패스 가능합니다. Amazon인증 MLS-C01덤프로 자격증취득에 가까워지고 나아가서는 IT업계에서 인정을 받는 열쇠를 소유한것과 같다고 할수 있습니다.

이 시험은 데이터 준비, 특징 공학, 모델 선택 및 최적화와 같은 주요 분야에서 개인의 지식과 능력을 검증하기 위해 설계되었습니다. 후보자들은 또한 기계 학습에 사용되는 AWS 서비스 및 도구, 예를 들어 아마존 세이지 메이커, 아마존 레코그니션, 그리고 아마존 컴프리핸드에 대한 깊은 이해를 가지고 있어야 합니다.

최신 AWS Certified Specialty MLS-C01 무료샘플문제 (Q121-Q126):

질문 # 121
A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application.
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
B. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
D. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

정답：C

설명：
Updating the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant is the simplest approach with the least risk to deploy the model and roll it back, if needed. This is because SageMaker supports A/B testing, which allows the Specialist to compare the performance of different model variants by sending a portion of the traffic to each variant. The Specialist can monitor the metrics of each variant and adjust the weights accordingly. If the new variant does not perform as expected, the Specialist can revert traffic to the last version by resetting the weights to 100% for the old variant and 0% for the new variant. This way, the Specialist can deploy the model without affecting the customer experience and roll it back easily if needed. References:
Amazon SageMaker
Deploying models to Amazon SageMaker hosting services

질문 # 122
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?

A. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
B. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
C. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
D. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

정답：D

질문 # 123
A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week.
Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

A. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
B. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
C. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
D. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
E. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.

정답：D,E

설명：
Explanation
The factors that will adversely impact the performance of the forecast model are:
Sales data is aggregated by week. This will reduce the granularity and resolution of the data, and make it harder to capture the daily patterns and variations in sales volume. The data scientist should request daily sales data from the source database to enable building a daily model, which will be more accurate and useful for the prediction task.
Sales data is missing zero entries for item sales. This will introduce bias and incompleteness in the data, and make it difficult to account for the items that have no demand or are out of stock. The data scientist should request that item sales data from the source database include zero entries to enable building the model, which will be more robust and realistic.
The other options are not valid because:
Detecting seasonality for the majority of stores will not be an issue, as sales data is highly consistent from week to week. Requesting categorical data to relate new stores with similar stores that have more historical data may not improve the model performance significantly, and may introduce unnecessary complexity and noise.
The sales data does not need to have more variance, as it reflects the actual demand and behavior of the customers. Requesting external sales data from other industries will not improve the model's ability to generalize, but may introduce irrelevant and misleading information.
Only 100 MB of sales data is not a problem, as it is sufficient to train a forecast model with Amazon S3 and Amazon Forecast. Requesting 10 years of sales data will not provide much benefit, as it may contain outdated and obsolete information that does not reflect the current market trends and customer preferences.
References:
Amazon Forecast
Forecasting: Principles and Practice

질문 # 124
A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

A. An XGBoost model where the objective parameter is set to multi: softmax
B. A regression forest where the number of trees is set equal to the number of product categories
C. A DeepAR forecasting model based on a recurrent neural network (RNN)
D. A deep convolutional neural network (CNN) with a softmax activation function for the last layer

정답：A

설명：
XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post-processing the predictions. References:
XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.
XGBoost Parameters: A reference guide for the parameters of XGBoost.

질문 # 125
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

A. Organize the Docker container's file structure to execute on GPU instances.
B. Bundle the NVIDIA drivers with the Docker image.
C. Build the Docker container to be NVIDIA-Docker compatible.
D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.

정답：C

설명：
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf
If you plan to use GPU devices, make sure that your containers are nvidia-docker compatible.
Only the CUDA toolkit should be included on containers. Don't bundle NVIDIA drivers with the image.
For more information about nvidia-docker, see NVIDIA/nvidia-docker.

질문 # 126
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MLS-C01시험패스 인증공부자료: https://kr.fast2test.com/MLS-C01-premium-file.html

Fast2test MLS-C01 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=1xiS5w3R54us6Wx4V7jCwmQC0RwI602KG